地表太阳辐射（SSR）是地表能量流动的一个重要因素，可以准确捕捉长期气候变化，了解地球大气系统的能量平衡。然而，由于原位观测数据的时间不均匀性和空间分布不均衡性，太阳表面辐射量的长期趋势估计存在很大的不确定性。本文通过整合所有可用的 SSR 观测数据，包括现有的同质化 SSR 结果，建立了一个观测集成和同质化的全球陆地（南极洲除外）站点 SSR 数据集（SSRIHstation）。然后对该序列进行内插，以获得 5° × 5° 分辨率的网格数据集（SSRIHgrid）。在此基础上，我们以 20 世纪再分析第 3 版（20CRv3）为基础，通过训练改进的部分卷积神经网络深度学习方法，进一步重建了 5° × 2.5° 分辨率的全球陆地（除南极洲外）SSR 长期（1955-2018 年）异常数据集（SSRIH20CR）。
", "ds_source": "为得出全球 SSR 变量，收集了九个 SSR 数据集。其中，六个数据集包含来自观测站的数据：两个全球地面测量数据集（GEBA、WRDC）和四个区域和国家层面的同质化产品（欧洲、中国、日本和意大利）。采用的数据集中有三个是再分析数据：第五代欧洲中期天气预报中心（ECMWF）再分析（ERA5）、20 世纪再分析第 3 版（20CRv3）数据和耦合模式相互比较项目第 6 阶段（CMIP6）历史模拟输出（125）。具体来说，ERA5 数据用于填充海洋和南极洲上空的数据，20CRv3 数据和 CMIP6 模拟用于人工影响模式的训练和重建。
", "ds_process_way": "对现有最广泛的全球 SSR 站观测数据进行了同质化和网格化处理。然后，使用卷积神经网络（CNN）方法对缺失的网格框和年份进行空间插值，以获得覆盖全球的陆地表面 SSR 异常数据集。最后，对重建的数据集进行初步分析和评估。
", "ds_quality": "数据质量良好。
", "ds_acq_start_time": "1955-01-01 00:00:00", "ds_acq_end_time": "2018-12-31 00:00:00", "ds_acq_place": "全球（南极洲除外）", "ds_acq_lon_east": null, "ds_acq_lat_south": null, "ds_acq_lon_west": null, "ds_acq_lat_north": null, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 28155357, "ds_files_count": 3, "ds_format": "nc", "ds_space_res": null, "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "42879a63-111d-4065-add7-12436d9e0f9d.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "用户在使用数据时请在正文中明确声明数据的来源，并在参考文献部分引用本元数据提供的引用方式。", "ds_from_station": null, "organization_id": "a4dd5849-78f2-44c5-b0f1-3450e952b2a2", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2024-07-26 17:03:17", "last_updated": "2024-09-27 15:29:18", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.FIGSHARE.DB6659.2024", "i18n": { "en": { "title": "Global Integrated and Homogenized Solar surface Radiation Datasets", "ds_format": "nc", "ds_source": "Nine SSR datasets are collected to derive the global SSR variable. In particular, six datasets contain data from observational stations: two global ground-based measurement datasets (GEBA, WRDC) and four homogenized products at the regional and country levels (Europe, China, Japan and Italy). Three of the adopted datasets are reanalysis data: fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5), 20th Century Reanalysis version 3 (20CRv3) data and the Coupled Model Intercomparison Project Phase 6 (CMIP6) historical simulation output (125). Specifically, the ERA5 data are used to fill the data over oceans and Antarctica, and 20CRv3 data and CMIP6 simulations are used for AI model training and reconstruction.
", "ds_quality": "The data quality is good.
", "ds_ref_way": "", "ds_abstract": "Surface solar radiation (SSR) is an essential factor in the flow of surface energy, enabling accurate capturing of long-term climate change and understanding of the energy balance of Earth's atmosphere system. However, the long-term trend estimation of SSR is subject to significant uncertainties due to the temporal inhomogeneity and the uneven spatial distribution of in situ observations. This paper develops an observational integrated and homogenized global terrestrial (except for Antarctica) station SSR dataset (SSRIHstation) by integrating all available SSR observations, including the existing homogenized SSR results. The series is then interpolated in order to obtain a 5° × 5° resolution gridded dataset (SSRIHgrid). On this basis, we further reconstruct a long-term (1955–2018) global land (except for Antarctica) SSR anomaly dataset with a 5° × 2.5° resolution (SSRIH20CR) by training improved partial convolutional neural network deep-learning methods based on 20th Century Reanalysis version 3 (20CRv3).
", "ds_time_res": "", "ds_acq_place": "Global (excluding Antarctica)", "ds_space_res": "", "ds_projection": "", "ds_process_way": "This paper first homogenizes and grids the most extensive collection of available global SSR station observations. Then, the missing grid boxes and years are spatially interpolated using a convolutional neural network (CNN) approach to obtain a globally covered land surface SSR anomaly dataset. Finally, the reconstructed datasets are initially analysed and evaluated.
", "ds_ref_instruction": "When using data, users should clearly declare the source of the data in the main text and cite the citation method provided by this metadata in the reference section." } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_outside", "cstr_reg_from": "reg_outside", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "地表太阳辐射", "SSRIH", "卷积神经网络" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球陆地（南极洲除外）" ], "ds_time_tags": [ 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018 ], "ds_contributors": [ { "true_name": "李庆祥", "email": "liqingx5@mail.sysu.edu.cn", "work_for": "中山大学大气科学学院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "李庆祥", "email": "liqingx5@mail.sysu.edu.cn", "work_for": "中山大学大气科学学院", "country": "中国" } ], "ds_managers": [ { "true_name": "李庆祥", "email": "liqingx5@mail.sysu.edu.cn", "work_for": "中山大学大气科学学院", "country": "中国" } ], "category": "生态" }